Appeal of one person to another is a subjective measure that varies with the individuals. There are many applications when an assessment of physical attractiveness may be useful from an objective source. Examples of such applications include online dating services; talent agencies; amusement providers; providers of professional services such as clinicians, estheticians and plastic surgeons; and employers looking to hire an actor, a performer, a model, or a subject for a demonstration.
Automated systems have been invented that provide an objective measure of physical attractiveness based on facial features provided from a digital image. For example, see the article “Assessing facial beauty through proportion analysis by image processing and supervised learning” by Gunes et al. (Int. J. Human-Computer Studies, Vol. 64, pp. 1184-1199, 2006). The objective measures provided by such models are based upon a single universal estimate of appeal intended to approximate the average appeal of that person on the population at large. However, facial features alone are only part of physical attractiveness. Other physical features such as height, weight, and hair color and style can contribute to physical appeal. In addition, non-physical factors such as income, activities, level of education, personality, and social or political affiliations may also influence the overall personal appeal one individual may have to another. Such factors may be reflected in a person's style of dress, posture, and body language in a manner that is too nuanced for computer algorithms to perceive, yet are obvious to the human observer. Previous systems suffer from the inability to adapt to local cultural norms and the context of a particular application since they offer a universal model of appeal and produce only a single estimate of appeal for an individual.
What is needed is a system that allows for the automatic generation of a measurement of appeal based on digital imagery and optionally preferences learned from the user of the system.